I was looking at a research paper and saw this diagram:
I believe I remember seeing a python plot for this - does anyone know a library or plot I can use to create this?
Related
I have this density heatmap generated using Python Plotly package:
Now I need to add wind vectors over the heatmap. I would like to use quiver plots if possible, but currently I want to know how to add any kind of plot to a mapbox.
I have only found this example but the source code in the Plotly Chart Studio doesn't load so I don't know how to do it, also I need it in 2D:
I am not 100% sure if it is helpful but there is a software called QGIS (it's free). It is much easier to work with geospatial data there. Also, try rasterio library. It may have some functions like that (library for geospatial analyze)
I once used the velocity capability of ipyleaflet https://ipyleaflet.readthedocs.io/en/latest/api_reference/velocity.html
It is not using plotly but could be a good replacement for this kind of map until you find a better solution.
I'm looking to augment my scatterplot (Python code, using Matplotlib and/or Seaborn) with marginal distributions (here plotted as histograms, but could also be kernel density estimates):
with a visualization of the differences (histogram/density estimate), like so:
I could probably roll my own, but this seems like such a common use case that I'm suspecting this might be implemented somewhere already in Matplotlib or Seaborn. A good fifteen minutes of Googling did not yield anything, and it also has not been asked before here on StackOverflow. Does anyone know of an off-the-shelf solution for this? (If no one does, I'll write my own and post it of course.) Thanks!
I am trying to make a graph with matplotlib like the one shown here. The problem I'm running into, is the top part of the graph. I already tried the table option from matplotlib but couldn't get it to work. Another idea is plotting my data over a backgroundimage made with inkscape (Problem is, I don't have any idea how to reference the backgroundimage so that the plot is scientifically correct/precise). So my questions are:
Do you think it is possible with the table option from matplotlib (and i should dig deeper)?
Or is my backgroundimage idea the better choice?
Or is there a complete different approach?
Thanks in advance
EDIT: For some reason I've been downvoted twice for posting this question (it hurts ppl) so I've rejigged it.
How do you combine multiple heatmaps in a stacked way with same color scale like to following image?
Additionally, does anyone know how to create the Augmented suffix tree?
Background:
I've worked through the python jupyter notebooks at the following link on how to create the heatmaps of (any) daily consumption profiles using seaborn
http://www.datadrivenbuilding.org/
...however there's a realllllllly cool combination graphic I'd love to be able to reproduce.
That image is an edited version of an image from this paper:
C. Miller, Z. Nagy, A. Schlueter, Automated daily pattern filtering of
measured building performance data, Automation in Construction 49,
Part A (2015) 1–17. doi:10.1016/j.autcon.2014.09.004. URL
http://www.sciencedirect.com/science/article/pii/S0926580514002015
They came up with the visualisation techniques themselves and describe them there. It looks like C. Miller is the one who wrote the notebook that you already found that shows how to draw the stacked heatmaps.
The augmented suffix tree is a type of visualization called a Sankey Diagram. You can plot these very beautifully using Plotly for example, or pySankey if you want to use matplotlib.
I am trying to create a 2D Contour Map in Python that looks like this:
In this case, it is a map of chemical concentration for a number of points on the map. But for the sake of simplicity, we could just say it's elevation.
I am given the map, in this case 562 by 404px. I am given a number of X & Y coordinates with the given value at that point. I am not given enough points to smoothly connect the line, and sometimes very few data points to draw from. It's my understanding that Spline plots should be used to smoothly connect the points.
I see that there are a number of libraries out there for Python which assist in creation of the contour maps similar to this.
Matplotlib's Pyplot Contour looks promising.
Numpy also looks to have some potential
But to me, I don't see a clear winner. I'm not really sure where to start, being new to this programming graphical data such as this.
So my question really is, what's the best library to use? Simpler would be preferred. Any insight you could provide that would help get me started the proper way would be fantastic.
Thank you.
In the numpy example that you show, the author is actually using Matplotlib. While there are several plotting libraries, Matplotlib is the most popular for simple 2D plots like this. I'd probably use that unless there is a compelling reason not to.
A general strategy would be to try to find something that looks like what you want in the Matplotlib example gallery and then modify the source code. Another good source of high quality Matplotlib examples that I like is:
http://astroml.github.com/book_figures/
Numpy is actually a N-dimensional array object, not a plotting package.
You don't need every pixel with data. Simply mask your data array. Matplotlib will automatically plot the area that it can and leave other area blank.
I was having this same question. I found that matplotlib has interpolation which can be used to smoothly connect discrete X-Y points.
See the following docs for what helped me through:
Matplotlib's matplotlib.tri.LinearTriInterpolator docs.
Matplotlib's Contour Plot of Irregularly Spaced Data example
How I used the above resources loading x, y, z points in from a CSV to make a topomap end-to-end